The Speech Recognition of Double-Syllable Chinese Words Based on the Hilbert Spectrum

摘要

Here a Chinese lexical recognition task is studied by a small vocabulary including 40 double-syllable Chinese words. In the approach presented, the Hilbert-Huang Transform (HHT) which consists of two steps is applied to speech signal analyzing. First, the speech signals are decomposed into a set of intrinsic mode functions (IMFs) by using the empirical mode decomposition (EMD) technique. Second, the first two IMFs are retained for further Hilbert spectral analysis. Final presentation of the speech signal is an energy-frequency-time distribution designated as the Hilbert spectrum, which can be used to depict the characteristics of speech sounds. For feature extraction, the Hilbert spectrum of each speech signal is divided into a set of frequency sub-bands. The number of discrete points on the Hilbert spectrum each sub-band contained is calculated as an element of the feature vector. Feature vectors obtained are fed to Support Vector Machine (SVM) classifier for classification. The proposed method is evaluated using 3840 speech samples from 8 different speakers (4 male). The experimental result, overall recognition rate of the 40 words achieving around 97% demonstrates the effectiveness of this approach.